Research

Forthcoming Electoral Systems and Geographically-Targeted Oversight: Evidence from Taiwan Legislative Yuan. Electoral Studies. (with Li Tang)

2025 Catalysts for Progress? Mapping Policy Insights from Energy Research. Energy Research & Social Science. 121: 103955. (with Brian Boyle, Stefan Müller, Sarah King and Robin Rauner)

2024 Electoral Reform and Fragmented Polarization: New Evidence from Taiwan Legislative Roll Call. Legislative Studies Quarterly. 50 (1): 3-21.

2024 (Mis)perception of Party-voter Congruence and Satisfaction with Democracy. Political Science Research and Methods. 13 (5): 885-902. (with Royce Carroll and Li Tang)

2023 The Role of Rituals in Adversarial Parliaments: An Analysis of Expressions of Collegiality in the British House of Commons. (Invited Contribution) Historical Social Research. 48 (3): 209-234. (with David Beck and Thomas Saalfeld)

Peer-Reviewed Articles (in Chinese)

2025 官僚「再詮釋」領導人意識形態初探：以《人民日報》習近平外交思想的評論為例 (Bureaucratic ‘Reinterpretation’ of Leaders’ Ideologies: A Case Study of People’s Daily’s Commentary on Xi Jinping Thought on Diplomacy). 中國大陸研究 Mainland China Studies. (with Yi-Nung Tsai)

• Electoral Reform and Issue Attention in Legislative Oversight: From SNTV to Mixed-Member Majoritarian System in Taiwan. (with Yu-Ceng Liao and Yi-ting Wang) Under Review PDF

  Abstract
  (Conference: 2023 EPSA, 2025 TPSA)

  This research note examines how Taiwan's electoral reform—from Single Non-Transferable Vote (SNTV) in multi-member district (MMD) to a single-member district (SMD) -- dominant mixed-member majoritarian system (MMM) -- affects how closely legislators align with their party's policy attention. While existing work explains which issues legislators emphasize and where they position themselves, less is known about how electoral systems shape the cohesion of issue attention within parties. We address this gap using 1999–2019 interpellations from Taiwanese legislators annotated with 422 fixed topic keywords. We introduce a new application of Wordfish: instead of estimating ideology, we scale legislators' issue attention and compute their divergence from the party. We find significantly higher intra-party convergence under SMD than SNTV. Local socioeconomic conditions strongly influence attention variation under SNTV but are substantially attenuated after reform. The findings highlight a key institutional trade-off: SNTV incentivizes geographically differentiated agendas, whereas SMD promotes party-aligned priorities.

• Multi-Agent LLM Systems for Synthetic Survey Experiments in Ethically Constrained Settings (with Linette Lim and Slava Jankin) PDF

  Abstract
  (Conference: 2025 PolMeth Europe, 2025 CwC-LLM Workshop EPSA, 2025 APSA)

  Experimental studies of misinformation often face ethical constraints because they expose human participants to false or harmful content. We propose an LLM-based multi-agent framework, implemented in AG2, that reproduces core features of survey experiments in a fully synthetic environment, avoiding direct exposure of human subjects to misinformation. We calibrate a population of 1,140 synthetic agents to rich Taiwanese voter survey data and examine, within this population, susceptibility to misinformation and the effectiveness of fact-checking interventions under randomized assignment. Agents with more pro-China predispositions exhibit higher acceptance of misinformation. Corrective information substantially reduces credibility ratings -- from 2.5 to 1.0 on a five-point scale in the treatment group relative to controls -- yet correlations between political attitudes and misinformation susceptibility persist after controlling for demographics. We make the multi-agent design and implementation fully transparent, releasing code and agent specifications, and argue that such synthetic survey experiments can complement, rather than replace, human-subject studies by enabling pre-testing of experimental designs and exploration of ethically sensitive scenarios.

• Cross-Lingual Stance Detection in Political Texts: Comparison and Application (with Stefan Müller)

  Abstract
  (Conference: 2024 ESPA, 2024 COMPTEXT)

  Measuring the stance on specific policies provides valuable insights for understanding policy-making, changes in political preferences, and party competition. In this paper, we fine-tune three multilingual transformer models — Sentence-BERT, Multilingual BERT, and XLM-RoBERTa on annotated texts of stances in more than 53,000 comments on Twitter and more than 67,000 comments to 150 political questions in German, French, and Italian. We benchmark our fine-tuned transformer models against open-source large language models (gpt-oss-120b and LLaMA 3.1) on ground truth annotations, finding that fine-tuned transformers achieve competitive performance with substantially better computational efficiency. After identifying the most suitable model, we validate our approach by applying this fine-tuned transformer to datasets from published journal articles: politicians’ support for the annual budget (Lowe and Benoit 2013), social media posts (Bestvater and Monroe 2023), and stances across policy areas (Green-Pedersen and Little 2023). Our findings demonstrate that fine-tuned multilingual transformers provide a scalable solution for large-scale stance detection in political texts. Drawing from our systematic comparison and validation, we provide methodological guidance for researchers applying stance detection to political texts. We release our fine-tuned models alongside benchmark ground truth data to enable researchers to deploy them directly or adapt them through further fine-tuning to new domains and languages, including those not covered in our German, French, and Italian training set.

• Political Text Analysis with Embedding Regression: From Multilingual to Cross-lingual Application. (with Chen Zheng, Winnie Xia and Slava Jankin) PolMeth Poster

  Abstract
  (Conference: 2025 PolMeth Summer Meeting, 2025 APSA)

  This research note builds upon existing embedding regression techniques (i.e., Rodriguez, Spirling and Stewart, 2023a,b; Wirsching et al., 2025) to systematically compare different embedding architectures for political text analysis. We examine three types: static (fastText and BPE), sequential contextual (LSTM-based architectures: Forward, Backward, and Forward+Backward), and dynamic embeddings (Transformer-based architectures: XLM-RoBERTa and mBERT). We analyze differences between these three types using Benoit et al. (2016)’s coal debates from Members of the European Parliament, available in English, German, Spanish, Italian, Polish, and Greek. Our experiments demonstrate that XLM-RoBERTa, Backward, Forward+Backward, and BPE achieve better performance in predicting political stance on coal policy, with stable cross-lingual flexibility and consistency suitable for comparative political analysis across multilingual settings. While XLM-RoBERTa and bidirectional sequence models maintain the highest accuracy, BPE offers an optimal balance of performance and computational efficiency. We are currently packaging this workflow as open-source software.

• Estimating Factions of Red Guard under Mao’s China: A Slogan-based Text Scaling Method with Historical Documents. (with Yi-Nung Tsai) Invited to Revise and Resubmit PDF

  Abstract
  (Conference: 2021 AsianPolmeth VIII, 2022 EPSA)

  Research on Red Guard publications during China's Cultural Revolution offers crucial insights into the period's political and cultural dynamics. We introduce a novel text analysis approach that addresses the challenges of analyzing non-spaced languages in historical contexts, advancing beyond traditional unsupervised text scaling –Wordfish– applications. Utilizing the Chinese Cultural Revolution Database, we combine keyword extraction techniques with Wordfish to estimate Red Guard units' ideological positioning. While our proposed approach largely aligns with historical accounts, we also reveal inconsistencies, particularly in how some Rebel-leaning Red Guard units rhetorically and ideologically act as fellow travelers with the Conservative coalition, deviating from established narratives and expert assessments. Our paper not only provides new insights into factional dynamics during the Cultural Revolution but also offers social scientists a new approach to studying Chinese politics and historical archives.

• Game-Theoretic Multi-Agent Systems with LLMs for Crisis Negotiation and Simulation. (with Shuli Zhang)
  

• How Do LLMs Differ Politically? A Multi-Agent Approach to Measuring AI Ideology. (with Ting Luo and Slava Jankin)
  

• Partisan Canvassing and Ideological Misperception in Britain: Evidence of Asymmetric Belief Updating from the British Election Studies. (with Li Tang)

• The Rural-Urban Divide in Populist Rhetoric: Cross-National Evidence from European Parliaments (with Edoardo Viganò)

• Vicar of Bray: Performative Loyalty and Career Survival in Maoist China. (with Yi-Nung Tsai)